Coordinated Federated Learning for Radio Resource Management in Future Wireless Networks

In this letter, we propose a coordinated federated learning framework for joint optimization of multiple radio resource management parameters in wireless networks. Like in a traditional federated learning, each user equipment (UE) trains a local machine learning model and shares parameters of the local models. However, the proposed approach also involves the communication-related aspects in the process of the federated learning and considers mutual dependencies among optimized variables. To this end, the UEs also share values of a new designed local loss functions specifically tailored to capture performance of the communication network to enable a local coordination for capturing mutual relation between optimized parameters. Then, base stations (BSs) leverage the received values of the new loss functions to produce a weighted global machine learning model. The weighting is based on the loss values so that the models of the UEs with low loss values are prioritized, since the low loss values indicate a proper setting of radio resource management parameters. The simulation results demonstrate a significant improvement from 10.6% to 18.9% and from 3.1% to 11.2% in sum capacity and ratio of UEs satisfied with the capacity, respectively, compared to the best performing state-of-the-art federated learning solution.